Der Wassergehalt des von den Pflanzen produzierten Nektars kann variieren. Um seine Konservierung in den Waben zu gewährleisten, darf er nicht gären. Deshalb wird er von den Bienen in Honig umgewandelt, indem sie seinen Wassergehalt senken und folglich den Zuckergehalt erhöhen. Dies verhindert die Entwicklung der Mikroorganismen. In der Dunkelheit des Bienenstocks ist es schwierig, den Herstellungsprozess des Honigs zu beobachten. Aus diesem Grund ist er auch nur wenig erforscht. Um die Umwandlung von Nektar in Honig zu untersuchen, haben wir die Technik der Tomographie eingesetzt. Sie ermöglicht es, den Zuckergehalt des eingelagerten Nektars sehr präzise zu messen, ohne den Bienenstock öffnen zu müssen und das Bienenvolk zu stören.
Looking beyond virus detection in RNA sequencing data: Lessons learned from a community-based effort to detect cellular plant pathogens and pests.
Haegeman A., Foucart Y., De Jonghe K., Goedefroit T., Al Rwahnih M., Boonham N., Candresse T., Gaafar Y. Z. A., Hurtado-Gonzales O. P., Zwitter Z. K., Kutnjak D., Lamovšek J., Lefebvre M., Malapi M., Mavrič Pleško I., Önder S., Reynard J.-S., Salavert Pamblanco F., Schumpp O., Stevens K., Pal C., Tamisier L., Ulubaş Serçe C., van Duivenbode I., Waite D. W., Hu X., Ziebell H., Massart S.
Looking beyond virus detection in RNA sequencing data: Lessons learned from a community-based effort to detect cellular plant pathogens and pests.
High-throughput sequencing (HTS), more specifically RNA sequencing of plant tissues, has become an indispensable tool for plant virologists to detect and identify plant viruses. During the data analysis step, plant virologists typically compare the obtained sequences to reference virus databases. In this way, they are neglecting sequences without homologies to viruses, which usually represent the majority of sequencing reads. We hypothesized that traces of other pathogens might be detected in this unused sequence data. In the present study, our goal was to investigate whether total RNA-seq data, as generated for plant virus detection, is also suitable for the detection of other plant pathogens and pests. As proof of concept, we first analyzed RNA-seq datasets of plant materials with confirmed infections by cellular pathogens in order to check whether these non-viral pathogens could be easily detected in the data. Next, we set up a community effort to re-analyze existing Illumina RNA-seq datasets used for virus detection to check for the potential presence of non-viral pathogens or pests. In total, 101 datasets from 15 participants derived from 51 different plant species were re-analyzed, of which 37 were selected for subsequent in-depth analyses. In 29 of the 37 selected samples (78%), we found convincing traces of non-viral plant pathogens or pests. The organisms most frequently detected in this way were fungi (15/37 datasets), followed by insects (13/37) and mites (9/37). The presence of some of the detected pathogens was confirmed by independent (q)PCRs analyses. After communicating the results, 6 out of the 15 participants indicated that they were unaware of the possible presence of these pathogens in their sample(s). All participants indicated that they would broaden the scope of their bioinformatic analyses in future studies and thus check for the presence of non-viral pathogens. In conclusion, we show that it is possible to detect non-viral pathogens or pests from total RNA-seq datasets, in this case primarily fungi, insects, and mites. With this study, we hope to raise awareness among plant virologists that their data might be useful for fellow plant pathologists in other disciplines (mycology, entomology, bacteriology) as well.
